Spirometry evaluation to assess performance of a claims-based predictive model identifying patients with undiagnosed COPD
Received 18 September 2018
Accepted for publication 28 December 2018
Published 15 February 2019 Volume 2019:14 Pages 439—446
Checked for plagiarism Yes
Review by Single-blind
Peer reviewer comments 2
Editor who approved publication: Dr Richard Russell
Chad Moretz,1 Srinivas Annavarapu,1 Rakesh Luthra,2 Seth Goldfarb,1 Andrew Renda,3 Asif Shaikh,2 Shuchita Kaila2
1Comprehensive Health Insights, Louisville, KY, USA; 2Boehringer Ingelheim, Ridgefield, CT, USA; 3Humana Inc., Louisville, KY, USA
Background: A claims-based model to predict patients likely to have undiagnosed COPD was developed by Moretz et al in 2015. This study aims to assess the performance of the aforementioned model using prospectively collected spirometry data.
Methods: A study population aged 40–89 years enrolled in a Medicare Advantage plan with prescription drug coverage or commercial health plan and without a claim for COPD diagnosis was identified from April 1, 2012 to March 31, 2016 in the Humana claims database. This population was stratified into subjects likely or unlikely to have undiagnosed COPD using the claims-based predictive model. Subjects were randomly selected for spirometry evaluation of FEV1 and FVC. The predictive model was validated using airflow limitation ratio (FEV1/FVC <0.70).
Results: A total of 218 subjects classified by the predictive model as likely and 331 not likely to have undiagnosed COPD completed spirometry evaluation. Those predicted to have undiagnosed COPD had a higher mean age (70.2 vs 67.9 years, P=0.0012) and a lower mean FEV1/FVC ratio (0.724 vs 0.753, P=0.0002) compared to those predicted not to have undiagnosed COPD. Performance metrics for the predictive model were: area under the curve =0.61, sensitivity =52.5%, specificity =64.6%, positive predictive value =33.5%, and negative predictive value =80.1%.
Conclusion: The claims-based predictive model identifies those not at risk of having COPD eight out of ten times, and those who are likely to have COPD one out of three times.
Keywords: COPD, exacerbation, predictive model, clinical validation, prevention
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